{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Untitled12.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "nx_Zyvu2e3T1",
        "colab_type": "code",
        "outputId": "806fea55-419d-42b0-ce7a-3b157a06bf5f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 666
        }
      },
      "source": [
        "!pip install tensorflow==1.12.0\n",
        "!pip install coremltools"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting tensorflow==1.12.0\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/22/cc/ca70b78087015d21c5f3f93694107f34ebccb3be9624385a911d4b52ecef/tensorflow-1.12.0-cp36-cp36m-manylinux1_x86_64.whl (83.1MB)\n",
            "\u001b[K     |████████████████████████████████| 83.1MB 72kB/s \n",
            "\u001b[?25hRequirement already satisfied: termcolor>=1.1.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.12.0) (1.1.0)\n",
            "Requirement already satisfied: grpcio>=1.8.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.12.0) (1.27.1)\n",
            "Requirement already satisfied: six>=1.10.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.12.0) (1.12.0)\n",
            "Requirement already satisfied: wheel>=0.26 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.12.0) (0.34.2)\n",
            "Requirement already satisfied: keras-preprocessing>=1.0.5 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.12.0) (1.1.0)\n",
            "Requirement already satisfied: astor>=0.6.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.12.0) (0.8.1)\n",
            "Requirement already satisfied: keras-applications>=1.0.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.12.0) (1.0.8)\n",
            "Requirement already satisfied: gast>=0.2.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.12.0) (0.2.2)\n",
            "Requirement already satisfied: protobuf>=3.6.1 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.12.0) (3.10.0)\n",
            "Requirement already satisfied: numpy>=1.13.3 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.12.0) (1.17.5)\n",
            "Collecting tensorboard<1.13.0,>=1.12.0\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/07/53/8d32ce9471c18f8d99028b7cef2e5b39ea8765bd7ef250ca05b490880971/tensorboard-1.12.2-py3-none-any.whl (3.0MB)\n",
            "\u001b[K     |████████████████████████████████| 3.1MB 32.0MB/s \n",
            "\u001b[?25hRequirement already satisfied: absl-py>=0.1.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.12.0) (0.9.0)\n",
            "Requirement already satisfied: h5py in /usr/local/lib/python3.6/dist-packages (from keras-applications>=1.0.6->tensorflow==1.12.0) (2.8.0)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.6/dist-packages (from protobuf>=3.6.1->tensorflow==1.12.0) (45.2.0)\n",
            "Requirement already satisfied: werkzeug>=0.11.10 in /usr/local/lib/python3.6/dist-packages (from tensorboard<1.13.0,>=1.12.0->tensorflow==1.12.0) (1.0.0)\n",
            "Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.6/dist-packages (from tensorboard<1.13.0,>=1.12.0->tensorflow==1.12.0) (3.2.1)\n",
            "Installing collected packages: tensorboard, tensorflow\n",
            "  Found existing installation: tensorboard 1.15.0\n",
            "    Uninstalling tensorboard-1.15.0:\n",
            "      Successfully uninstalled tensorboard-1.15.0\n",
            "  Found existing installation: tensorflow 1.15.0\n",
            "    Uninstalling tensorflow-1.15.0:\n",
            "      Successfully uninstalled tensorflow-1.15.0\n",
            "Successfully installed tensorboard-1.12.2 tensorflow-1.12.0\n",
            "Collecting coremltools\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/77/19/611916d1ef326d38857d93af5ba184f6ad7491642e0fa4f9082e7d82f034/coremltools-3.3-cp36-none-manylinux1_x86_64.whl (3.4MB)\n",
            "\u001b[K     |████████████████████████████████| 3.4MB 2.8MB/s \n",
            "\u001b[?25hRequirement already satisfied: protobuf>=3.1.0 in /usr/local/lib/python3.6/dist-packages (from coremltools) (3.10.0)\n",
            "Requirement already satisfied: six>=1.10.0 in /usr/local/lib/python3.6/dist-packages (from coremltools) (1.12.0)\n",
            "Requirement already satisfied: numpy>=1.14.5 in /usr/local/lib/python3.6/dist-packages (from coremltools) (1.17.5)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.6/dist-packages (from protobuf>=3.1.0->coremltools) (45.2.0)\n",
            "Installing collected packages: coremltools\n",
            "Successfully installed coremltools-3.3\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N7M-1RR-yP5y",
        "colab_type": "code",
        "outputId": "5017f9d3-3f8b-4a2c-9095-59e3bc1bb882",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        }
      },
      "source": [
        "import tensorflow as tf\n",
        "print(tf.__version__)"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/dtypes.py:523: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
            "  _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n",
            "/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/dtypes.py:524: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
            "  _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n",
            "/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/dtypes.py:525: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
            "  _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n",
            "/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/dtypes.py:526: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
            "  _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n",
            "/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/dtypes.py:527: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
            "  _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n",
            "/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/dtypes.py:532: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
            "  np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<p style=\"color: red;\">\n",
              "The default version of TensorFlow in Colab will soon switch to TensorFlow 2.x.<br>\n",
              "We recommend you <a href=\"https://www.tensorflow.org/guide/migrate\" target=\"_blank\">upgrade</a> now \n",
              "or ensure your notebook will continue to use TensorFlow 1.x via the <code>%tensorflow_version 1.x</code> magic:\n",
              "<a href=\"https://colab.research.google.com/notebooks/tensorflow_version.ipynb\" target=\"_blank\">more info</a>.</p>\n"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "1.12.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dvPyeQUqfWfO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        },
        "outputId": "adf493bf-b6e5-4cc9-8e23-b2fe9881fbfc"
      },
      "source": [
        "import tensorflow as tf\n",
        "import keras\n",
        "from keras.models import Sequential, Model\n",
        "from keras.layers import Dense, Flatten, Input, Embedding, Flatten, concatenate\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from sklearn import preprocessing\n",
        "import coremltools"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n",
            "WARNING:root:Keras version 2.2.5 detected. Last version known to be fully compatible of Keras is 2.2.4 .\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nSthi-VISSNi",
        "colab_type": "text"
      },
      "source": [
        "# Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i1n4RkrZANQW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Swift for TensorFlow Model\n",
        "\n",
        "# struct RegressionModel: Module {\n",
        "#     var numericalLayer = Dense<Float>(inputSize: 11, outputSize: 32, activation: relu)\n",
        "#     var embedding1 = Embedding<Float>(vocabularySize: 2, embeddingSize: 2)\n",
        "#     var embedding2 = Embedding<Float>(vocabularySize: 9, embeddingSize: 5)\n",
        "#     var embeddingLayer = Dense<Float>(inputSize: (4 + 45), outputSize: 64, activation: relu)\n",
        "#     var allInputConcatLayer = Dense<Float>(inputSize: (32 + 64), outputSize: 128, activation: relu)\n",
        "#     var hiddenLayer = Dense<Float>(inputSize: 128, outputSize: 32, activation: relu)\n",
        "#     var outputLayer = Dense<Float>(inputSize: 32, outputSize: 1)\n",
        "    \n",
        "#     @differentiable\n",
        "#     func callAsFunction(_ input: MultiInputs<[Tensor<Float>], [Tensor<Int32>]>) -> Tensor<Float> {\n",
        "#         let numericalInput = numericalLayer(input.numerical[0])\n",
        "#         let embeddingOutput1 = embedding1(input.categorical[0])\n",
        "#         let embeddingOutput1Reshaped = embeddingOutput1.reshaped(to: \n",
        "#             TensorShape([embeddingOutput1.shape[0], embeddingOutput1.shape[1] * embeddingOutput1.shape[2]]))\n",
        "#         let embeddingOutput2 = embedding2(input.categorical[1])\n",
        "#         let embeddingOutput2Reshaped = embeddingOutput2.reshaped(to: \n",
        "#             TensorShape([embeddingOutput2.shape[0], embeddingOutput2.shape[1] * embeddingOutput2.shape[2]]))\n",
        "#         let embeddingConcat = Tensor<Float>(concatenating: [embeddingOutput1Reshaped, embeddingOutput2Reshaped], alongAxis: 1)\n",
        "#         let embeddingInput = embeddingLayer(embeddingConcat)\n",
        "#         let allConcat = Tensor<Float>(concatenating: [numericalInput, embeddingInput], alongAxis: 1)\n",
        "#         return allConcat.sequenced(through: allInputConcatLayer, hiddenLayer, outputLayer)\n",
        "#     }\n",
        "# }"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JXp-AqomffLx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "numerical_input = Input(shape=(11,))\n",
        "categorical_input1 = Input(shape=(1,))\n",
        "categorical_input2 = Input(shape=(1,))\n",
        "\n",
        "# numerical = Dense(32, activation='relu')(numerical_input)\n",
        "\n",
        "embedding1 = Embedding(input_dim=2, output_dim=2, input_length=1)(categorical_input1)\n",
        "embedding1 = Flatten()(embedding1)\n",
        "\n",
        "embedding2 = Embedding(input_dim=9, output_dim=5, input_length=1)(categorical_input2)\n",
        "embedding2 = Flatten()(embedding2)\n",
        "\n",
        "# embeddingConcat = concatenate([embedding1, embedding2], axis=1)\n",
        "# embedding = Dense(64, activation='relu')(embeddingConcat)\n",
        "\n",
        "# allConcat = concatenate([numerical, embedding], axis=1)\n",
        "allConcat = concatenate([numerical_input, embedding1, embedding2], axis=1)\n",
        "hidden = Dense(64, activation='relu')(allConcat)\n",
        "hidden = Dense(32, activation='relu')(hidden)\n",
        "output = Dense(1)(hidden)\n",
        "\n",
        "keras_model = Model(inputs=[numerical_input, categorical_input1, categorical_input2], outputs=output)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2SePBabhfk5D",
        "colab_type": "code",
        "outputId": "cbfc35dd-51bd-46fb-feff-dea213923225",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 566
        }
      },
      "source": [
        "keras_model.summary()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"model_1\"\n",
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "input_2 (InputLayer)            (None, 1)            0                                            \n",
            "__________________________________________________________________________________________________\n",
            "input_3 (InputLayer)            (None, 1)            0                                            \n",
            "__________________________________________________________________________________________________\n",
            "embedding_1 (Embedding)         (None, 1, 2)         4           input_2[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "embedding_2 (Embedding)         (None, 1, 5)         45          input_3[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "input_1 (InputLayer)            (None, 11)           0                                            \n",
            "__________________________________________________________________________________________________\n",
            "flatten_1 (Flatten)             (None, 2)            0           embedding_1[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "flatten_2 (Flatten)             (None, 5)            0           embedding_2[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "concatenate_1 (Concatenate)     (None, 18)           0           input_1[0][0]                    \n",
            "                                                                 flatten_1[0][0]                  \n",
            "                                                                 flatten_2[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "dense_1 (Dense)                 (None, 64)           1216        concatenate_1[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "dense_2 (Dense)                 (None, 32)           2080        dense_1[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "dense_3 (Dense)                 (None, 1)            33          dense_2[0][0]                    \n",
            "==================================================================================================\n",
            "Total params: 3,378\n",
            "Trainable params: 3,378\n",
            "Non-trainable params: 0\n",
            "__________________________________________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sfxSyK6xSLsu",
        "colab_type": "text"
      },
      "source": [
        "# Save and Export no Train Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k8SQOLPlfoxX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "keras_model.save('keras_simplified_no_train_model.h5')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MkXmhvweF1ti",
        "colab_type": "code",
        "outputId": "927cb6ed-5a5f-4e01-bc18-f865b29fb4bb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        }
      },
      "source": [
        "mlmodel = coremltools.converters.keras.convert('keras_simplified_no_train_model.h5', use_float_arraytype = True)\n",
        "mlmodel.save('coreml_simplified_no_train.mlmodel')"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0 : input_2, <keras.engine.input_layer.InputLayer object at 0x7f1c4b9eed30>\n",
            "1 : input_3, <keras.engine.input_layer.InputLayer object at 0x7f1c4b9eeda0>\n",
            "2 : embedding_1, <keras.layers.embeddings.Embedding object at 0x7f1c4b9eee48>\n",
            "3 : embedding_2, <keras.layers.embeddings.Embedding object at 0x7f1c4b9eeeb8>\n",
            "4 : input_1, <keras.engine.input_layer.InputLayer object at 0x7f1c4b9eef60>\n",
            "5 : flatten_1, <keras.layers.core.Flatten object at 0x7f1c4b9eefd0>\n",
            "6 : flatten_2, <keras.layers.core.Flatten object at 0x7f1c4b9f6198>\n",
            "7 : concatenate_1, <keras.layers.merge.Concatenate object at 0x7f1c4b9f60b8>\n",
            "8 : dense_1, <keras.layers.core.Dense object at 0x7f1c4b9f61d0>\n",
            "9 : dense_1__activation__, <keras.layers.core.Activation object at 0x7f1c4e211240>\n",
            "10 : dense_2, <keras.layers.core.Dense object at 0x7f1c4b9f6390>\n",
            "11 : dense_2__activation__, <keras.layers.core.Activation object at 0x7f1c4b9eecf8>\n",
            "12 : dense_3, <keras.layers.core.Dense object at 0x7f1c4b9f6518>\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/keras/engine/saving.py:310: UserWarning: No training configuration found in save file: the model was *not* compiled. Compile it manually.\n",
            "  warnings.warn('No training configuration found in save file: '\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Fsek-y7o0_FM",
        "colab_type": "code",
        "outputId": "986f0593-887f-4d39-cc41-eadd54bd8787",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "spec = mlmodel.get_spec()\n",
        "spec"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "specificationVersion: 1\n",
              "description {\n",
              "  input {\n",
              "    name: \"input1\"\n",
              "    type {\n",
              "      multiArrayType {\n",
              "        shape: 11\n",
              "        dataType: FLOAT32\n",
              "      }\n",
              "    }\n",
              "  }\n",
              "  input {\n",
              "    name: \"input2\"\n",
              "    type {\n",
              "      multiArrayType {\n",
              "        shape: 1\n",
              "        dataType: FLOAT32\n",
              "      }\n",
              "    }\n",
              "  }\n",
              "  input {\n",
              "    name: \"input3\"\n",
              "    type {\n",
              "      multiArrayType {\n",
              "        shape: 1\n",
              "        dataType: FLOAT32\n",
              "      }\n",
              "    }\n",
              "  }\n",
              "  output {\n",
              "    name: \"output1\"\n",
              "    type {\n",
              "      multiArrayType {\n",
              "        shape: 1\n",
              "        dataType: FLOAT32\n",
              "      }\n",
              "    }\n",
              "  }\n",
              "  metadata {\n",
              "    userDefined {\n",
              "      key: \"coremltoolsVersion\"\n",
              "      value: \"3.3\"\n",
              "    }\n",
              "  }\n",
              "}\n",
              "neuralNetwork {\n",
              "  layers {\n",
              "    name: \"embedding_1\"\n",
              "    input: \"input2\"\n",
              "    output: \"embedding_1_output\"\n",
              "    embedding {\n",
              "      inputDim: 2\n",
              "      outputChannels: 2\n",
              "      weights {\n",
              "        floatValue: 0.013908136636018753\n",
              "        floatValue: 0.01281685009598732\n",
              "        floatValue: 0.027863625437021255\n",
              "        floatValue: -0.04879063367843628\n",
              "      }\n",
              "    }\n",
              "  }\n",
              "  layers {\n",
              "    name: \"embedding_2\"\n",
              "    input: \"input3\"\n",
              "    output: \"embedding_2_output\"\n",
              "    embedding {\n",
              "      inputDim: 9\n",
              "      outputChannels: 5\n",
              "      weights {\n",
              "        floatValue: 0.03423110023140907\n",
              "        floatValue: -0.026351524516940117\n",
              "        floatValue: 0.019886482506990433\n",
              "        floatValue: -0.04353445768356323\n",
              "        floatValue: 0.031783293932676315\n",
              "        floatValue: 0.046316806226968765\n",
              "        floatValue: -0.04644042253494263\n",
              "        floatValue: 0.019939515739679337\n",
              "        floatValue: 0.049564432352781296\n",
              "        floatValue: 0.04086582735180855\n",
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          "metadata": {
            "tags": []
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          "execution_count": 9
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}